Chapter 1 Introduction to R
Before we get started, this book contains some basic cues to help facilitate your understanding of the current topic.
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At the end of this chapter you should be able to
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Why choose it?
In recent years, R has gained a lot of popularity among data scientists and analysts. The reason for this is simple: R is a language that is specifically designed for working with data. While other programming languages like C/C++, Java, and Python are general purpose languages that can be used in any domain, R is geared towards data analysis and manipulation.
Because R is designed for working with data, it has several features that make it easier to work with large datasets. For instance, R has several built-in data structures that allow users to organize and manipulate data in a variety of ways. Additionally, R has a wide range of libraries and packages that can be used to perform specific tasks like data visualization, statistical analysis, and machine learning.
Another reason why R is so popular among data scientists is that it is an open-source language. This means that anyone can contribute to its development, and there is a vast community of users and developers working together to improve the language and its capabilities.
Despite its many advantages, R does have a few limitations. For example, it is not as fast as some other programming languages, and it can be difficult for beginners to learn. However, there are many resources available online to help users learn R, and once they get the hang of it, they will find that it is a powerful tool for data analysis and visualization.
Overall, R is an excellent language for anyone who wants to work with data. Its specialized features and wide range of capabilities make it a top choice for data scientists and analysts everywhere.
The Stack Overflow blog post The Impressive Growth of R by David Robinson, discusses the growth and popularity of the programming language R. The post highlights the increase in R’s usage on Stack Overflow, as well as the growing interest in R from various industries.
We found in a previous post that Python has a solid claim to being the fastest-growing programming language in terms of Stack Overflow visits. The same analysis showed that the R programming language has shown remarkable growth in the last five years as well. In fact, R is growing at a similar rate to Python…
The post provides an overview of R’s history, its advantages and disadvantages, and its current position in the programming world. The author notes that R’s popularity is due to its ability to handle large datasets, its flexibility for data analysis and in increase in popularity of data science and the growing number of companies using R for data analysis. Overall, the post concludes that R’s growth and popularity are likely to continue in the future, as more industries recognize the value of data analysis and turn to R as a solution.
What you can do with it?
The potential of what you can achieve with R is vast and ultimately depends on the level of dedication you have towards learning and expanding your skill set. By utilizing R, you can analyze data through various methods such as reading and plotting data, constructing analysis pipelines, prototyping new algorithms, and even writing your analysis code into shareable packages. With these abilities, you can not only perform data analysis, but also create a more efficient and reproducible workflow. The more you learn and experiment with R, the more you can discover and unlock its full potential.
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NOTES Some helpful explanatory notes and tips appear as a block quote.
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The R Learning Curve
The learning curve for R 10+ years ago was difficult as there where fewer R resources, it was less mature with not a lot of interest. Additionally, there were fewer people in the community and data science wasn’t “a thing” yet.
Figure 1.1: R learning curve past
The R programming language is still challenging but worth it. With the introduction of packages encompassed in the tidyverse there are more high-quality resources, mature utilization with well documented explanations and examples. Currently there is lots of current interest in R with a large community of users and developers. Additionally, the data science “revolution has pushed R to develop and evolve, become more user-centric.
Figure 1.2: R learning curve present
Learning to code
When it comes to learning a programming language, it can be daunting to know where to start. However, the first step to learning any programming language is to understand its syntax. Syntax refers to the set of rules and symbols that make up structurally correct code. Without proper syntax, even the smallest of errors can result in code that doesn’t run. These errors could be as simple as a typo, an incorrect name, missing spaces or too many spaces, or even wrong brackets. Syntax errors can be frustrating, especially for beginners, but it’s important to hang in there and start simple.
It’s best to begin by trying to understand very simple cases first, before building and expanding on them. This approach will help you to get a better grip on the basics of the language and will help you to avoid becoming overwhelmed. If you’re learning R, there are many resources available to help you get started. You could start by reading through the R Book, which provides a comprehensive guide to the R programming language. Alternatively, there are many online tutorials available, which can help to break down complex concepts into more manageable pieces.
In short, when learning R, it’s important to remember that syntax is key. By taking the time to understand the syntax rules, you can avoid frustrating syntax errors and build a solid foundation for your future coding endeavors
1.1 Alternatives
When it comes to data science, R is a popular programming language among statisticians and data analysts. However, there are several data science alternatives to R that are also gaining popularity.
One of the most popular alternatives to R is Python. Python is a general-purpose programming language that has a wide range of libraries and frameworks for data science. It is known for its simplicity, readability, and versatility. Python’s libraries such as NumPy, Pandas, and Scikit-Learn are widely used in data science for tasks such as data cleaning, data analysis, and machine learning.
Another alternative to R is Julia, a new programming language that is designed specifically for scientific computing and numerical analysis. Julia is known for its speed and efficiency, making it a great choice for data analysis and modeling. Julia also has a growing package ecosystem with libraries such as DataFrames.jl and Flux.jl that are specifically designed for data science.
Matlab is another alternative to R that is widely used in the scientific community. Matlab is known for its extensive numerical computing capabilities and its strong visualization features. It is commonly used in fields such as engineering, physics, and finance for data analysis and modeling.
In conclusion, while R is a popular language for data science, it is not the only option available. Python, Julia, and Matlab are all viable alternatives with their own strengths and weaknesses. It is important to consider the specific needs of your project and choose the programming language that best suits your requirements.
Did you know, that while R on its own is a powerful scripting language, some analytical tasks might require the use of other programming languages such as Python, C++ or Rust. Luckily, R provides different packages that allow us to use these languages within R code. These packages provide a seamless integration between these languages and R, allowing you to leverage the strengths of each language to perform complex tasks.
The reticulate package enables the integration of Python code in R. This package allows you to import Python modules and functions directly into R and also allows you to call Python functions from R code. This is especially useful when you need to use Python’s machine learning libraries such as TensorFlow or PyTorch, which are not yet available in R.
Similarly, the Rcpp package provides a smooth integration between R and C++. With this package, you can easily write C++ functions and use them directly in your R code. This is useful when you need to perform computationally-intensive tasks, such as simulations or optimization, that require the speed of C++.
Finally, the extendr package provides an interface between R and Rust, allowing you to use Rust functions in R code and vice versa. Rust is a relatively new programming language that provides a balance between performance and safety. It is especially useful when you need to develop high-performance and low-level code, such as in systems programming or hardware development.
1.2 Resources
When working with R, it is important to understand the basics and terms so that you can ask the right questions when seeking help. In the next two sections, we will provide an overview of these concepts to ensure that you have a solid foundation. It is worth noting that while googling your issue can be a great starting point, it is also important to seek out additional resources to help you solve your problem. For instance, you might consider joining an R community or forum where you can ask questions and receive feedback from other users. Additionally, many universities and organizations offer R workshops or training programs that can help you build your skills and knowledge. By taking advantage of these resources, you can develop a deeper understanding of R and become more confident in your ability to use it for data analysis and visualization.
1.2.1 Online
In addition to Googling to find how to do something in R, there are several online resources available for individuals learning R programming and needing assistance with concepts or coding issues. These resources include CRAN, Bioconductor, RStudio Community, R-bloggers, and Stack Overflow. Each of these resources offers different benefits, such as packages, forums, blogs, and Q&A communities, to help R users.
Locating available packages (pre-built algorithms)
The Comprehensive R Archive Network: cran.r-project.org/
Bioconductor: bioconductor.org/
RforMassSpectrometry: rformassspectrometry.org/
Ways to ask for help, or find answers to a similar question
The RStudio Community: community.rstudio.com/
R Community Explorer: r-community.org/rstudio/
R bloggers: r-bloggers.com/
Stack Overflow: stackoverflow.com/
More ways to search and find what you are looking for
R-Seek: rseek.org/
R-universe: r-universe.dev/search/
Cheat-sheets
Tidyverse: www.tidyverse.org/
reading data: readr.tidyverse.org/
manipulating data :dplyr.tidyverse.org/
arranging table data: tidyr.tidyverse.org/
visualizing data ggplot2.tidyverse.org/
working with strings: stringr.tidyverse.org/
working with date and time: lubridate.tidyverse.org/
1.2.2 In Print
R books in print are becoming increasingly popular due to the growing demand for R programming and data analysis. These books offer several benefits that make them an excellent resource for anyone who wants to learn R programming or improve their data analysis skills. One of the key advantages of R books in print is that they are easy to read and navigate. The authors of these books take into consideration that not everyone who reads their books is an expert in programming. They use simple language and examples to explain concepts from the basics, making it easy for readers to understand. They can be used as a quick reference guide when working on a project or when facing a programming challenge. R books in print are cost-effective. While online resources are free, they are not always reliable, and it can be time-consuming to find the information you need. R books in print, on the other hand, are written and edited by experts who have years of experience in the field making them a reliable source of information.
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data 1st Edition by Garrett Grolemund, Hadley Wickham
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
Use R!, a collection of 67 print books.
The Use R! collection of print books is a series of books aimed at helping people learn and use the R programming language. The books in this series cover a wide range of topics related to R, including data analysis, statistical modeling, and data visualization.
Each book in the collection is written by a different author or group of authors, and provides a unique perspective on how to use R for different tasks. The books are targeted at a range of audiences, from beginners who are just starting to learn R, to more advanced users who are looking to expand their skills and knowledge.
Some of the popular books in the Use R! collection include:
- An Introduction to R by Venables and Smith: This book provides a comprehensive introduction to the R programming language, covering topics such as data types, control structures, and functions.
- Data Manipulation with R by Spector: This book covers how to use R to manipulate data, including topics such as data cleaning, merging, and reshaping.
- ggplot2: Elegant Graphics for Data Analysis by Wickham: This book provides an in-depth introduction to the ggplot2 package in R, which is used for creating high-quality data visualizations.
- Applied Regression Analysis by Fox: This book covers how to use R to perform regression analysis, including topics such as linear regression, logistic regression, and mixed-effects models.
In summary, the Use R! collection of print books is a valuable resource for anyone looking to learn or improve their skills in the R programming language. With a wide range of topics and authors, there is something for everyone in this collection.
1.2.3 Organizations
Many R organizations offer individuals an opportunity for training and support, networking, access to resources, and help building a reputation. These benefits make R organizations a valuable resource to consider for both individuals and organizations using R for statistical computing and graphics.
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R User Group (RUG) RUGs are a relaxed and friendly way to broaden your contacts, scope and understanding of R. |
R User Groups
R User Groups are communities of people who are interested in using R, a programming language and software environment for statistical computing and graphics. These groups are formed to provide a platform for individuals to learn, share knowledge, and collaborate on projects related to R programming.
R User Groups usually meet on a regular basis, either virtually or in person, and organize events such as talks, workshops, and hackathons. These events are designed to provide members with opportunities to improve their skills, network with like-minded individuals, and work on projects that are of mutual interest.
R User Groups are open to anyone who is interested in using R, regardless of their level of expertise. Members can range from beginners who are just starting to learn R, to experienced professionals who use R on a daily basis. This diversity of membership allows for a rich exchange of ideas and perspectives on the use of R in various fields, such as data science, finance, and healthcare.
Joining an R User Group can be a great way to stay up-to-date with the latest developments in R programming, as well as to learn from and collaborate with other members. Many R User Groups also have online forums or discussion boards where members can ask questions, share resources, and seek feedback on their work.
Where to find a RUG
R Specific Confrences
The R Conference currently takes place in New York, Washington D.C., and soon Dublin, Ireland. They were created to foster the local R communities and serve as gathering places for people to learn from their peers. The R Conference hosts one of the most elite gatherings of data scientists and data professionals who come together to explore, share, inspire and to promote the growth of open source ideals.
Innovation and Entrepreneurship in Data, Design, Development and Discovery
The D4 conference exists to bring creative communities together and to bolster the exchange of ideas. Data professionals, software developers, and other creatives can meet and collaborate.
R Education at Confrences
American Society for Mass Spectrometry
The American Society for Mass Spectrometry (ASMS) was created in 1969 to promote and share knowledge of mass spectrometry. Membership includes over 8,500 scientists from academia, industry, and government labs. Members focus on technique and instrument advancements, as well as research in various sciences. ASMS offers several short-courses (1 or 2-day) covering a myriad of topics, including using R for data analysis.
Mass Spectrometry & Advances in the Clinical Lab
MSACL aims to advance mass spectrometry and other advanced technologies in clinical laboratory medicine through education and training of practitioners, physicians, and other healthcare professionals. They also support the development of new technologies for diagnosis, treatment, and prognosis of clinical disorders. MSACL offers resources through their Learning Center on several topics, including using R in clinical data analysis.
Computation and statistics for mass spectrometry and proteomics
The event features keynotes, tutorials, and hands-on sessions led by proteomics experts and authors of methods and computational tools. We will take an in-depth look at case studies focused on the design and analysis of quantitative mass spectrometry-based experiments. At the end of each day, the speakers will be available online for additional exercises and Q&A sessions, if there is interest.